This is a basic-level self-standing learning module on Machine Learning.
The module (5–10 hours) introduces the core principles of machine learning and its role in building data-driven applications. Students will learn the difference between supervised, unsupervised, and reinforcement learning, as well as the key steps of a machine learning workflow: data preparation, model training, evaluation, and interpretation. The focus is on building an intuitive understanding of concepts, supported by simple examples and visual explanations rather than programming or mathematics.
By the end of the module, learners will:
- Understand what machine learning is and how it differs from traditional programming.
- Recognize the main types of machine learning problems.
- Appreciate how ML is used in everyday applications, from recommendation systems to healthcare.
- Identify basic ethical issues such as bias, fairness, and explainability.
Target audience
Non-professionals, general audience with no prior knowledge assumed in Data Analysis, Data Science, Artificial Intelligence, or Programming.
Intended Learning Outcomes
| ILO2 | Machine Learning | To describe and explain fundamentals of machine learning and the classical concepts for algorithm training. Also be able to apply machine learning approaches to solve simple problems on small datasets. |
Lessons
- Introduction to Machine Learning
- Types of Machine Learning
- Machine Learning Workflow
- Basic Algorithms I
- Basic Algorithms II
- Applications of Machine Learning
Course Content
About Instructor
Course Includes
- 6 Lessons
- 57 Topics
- 5 Quizzes
